TY - GEN
T1 - Dynamic sampling design for characterizing spatiotemporal processes in manufacturing
AU - Shao, Chenhui
AU - Jin, Jionghua
AU - Hu, S. Jack
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the 3D measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to pre-dictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.
AB - Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the 3D measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to pre-dictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.
KW - Dynamic sampling design
KW - Intelligent sensing
KW - Manufacturing
KW - Spatiotemporal processes
UR - http://www.scopus.com/inward/record.url?scp=85027869085&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027869085&partnerID=8YFLogxK
U2 - 10.1115/MSEC2017-2695
DO - 10.1115/MSEC2017-2695
M3 - Conference contribution
AN - SCOPUS:85027869085
T3 - ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
BT - Manufacturing Equipment and Systems
PB - American Society of Mechanical Engineers
T2 - ASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
Y2 - 4 June 2017 through 8 June 2017
ER -